decision-trees | A WordPress plugin to provide Decision Trees | Runtime Evironment library

value=[1,3] means that, in this exactly leaf of the tree (before applying the filter x[2] <=0.5), you have:

• 1 sample of the class 0
• 3 sample of the class 1

Once you are going down the tree, you are filtering. Your objective is have perfectly separated classes. So you tend to have something like value=[0,1], which means that after applying all filters, you have 0 samples of class 0 and 1 samples of class 1.

You can also check that the sum of value is always similar to the samples. This makes completely sense since value is only telling you how all samples that arrived this leaf are distributed.

as.factor((birthwt[cols])) is calling as.factor on a list of 5 vectors. If you do that R will interpret each of those 5 vectors as the levels, and the column headers as the labels, of a factor variable, which is clearly not what you want:

The problem is library incompatibility. This docker container have solved my problem:

https://github.com/Kaggle/docker-python/commit/a6ba32e0bb017a30e079cf8bccab613cd4243a5f

I have met the same problem, but I have solved it. How I solved it? This is my answer.

1. please install the graphviz of 2.3.8.msi。 graphvizzgraphviz

2. install what you have download the msi package and then add your install path to the environmenttal path like this 'C:\Program Files (x86)\Graphviz2.38; C:\Program Files (x86)\Graphviz2.38\bin'

3. pip install graphviz.

4. the following is my result.

Since there can be multiple decision boundaries around a sample, I'm going to assume distance here refers to distance to nearest decision boundary.

The solution is a recursive tree traversal algorithm. Note that decision tree doesn't allow a sample to be on boundary, like e.g. SVM, each sample in feature space must belong to one of the classes. So here we will keep modifying the sample's feature in small steps, and whenever that leads to a region with a different label (than one originally assigned to the sample by trained classifier), we assume we've reached decision boundary.

In detail, like any recursive algorithm, we have two main cases to consider:

1. Base case i.e. we're at a leaf node. We simply check if the current sample have different label: if so then return it, otherwise return None.
2. Non leaf nodes. There are two branches, we send the sample to both. We don't modify the sample to send it to branch it would naturally take. But before sending it to the other branch, we look at the (feature, threshold) pair of the node, and modify the sample's given feature just enough to push it on the opposite side of threshold.

Complete python code:

I had the same problem recently and the only way I found is by trying diffent figure size (it can still be bluery with big figure. For exemple, to plot the 4th tree, use:

Make sure you have graphviz installed. Because the plot_tree of XGboost internally uses graphviz for plotting.

After much searching, I found software offered by Turi that models a regression tree, not to be confused with a decision tree. Hope this helps

For what its worth, a regression tree looks like this:

While a decision/classifier tree looks like this:

And tho they look the same, the attribute needed to create this is tree_ which is only available to classifiers, not regressors.

Using ncfirth's link, I was able to modify the code there so that it fits to my problem: